Trait

com.intel.analytics.bigdl.dataset

Transformer

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trait Transformer[A, B] extends Serializable

Transform a data stream of type A to type B. It is usually used in data pre-process stage. Different transformers can compose a pipeline. For example, if there're transformer1 from A to B, transformer2 from B to C, and transformer3 from C to D, you can compose them into a bigger transformer from A to D by transformer1 -> transformer2 -> transformer 3.

The purpose of transformer is for code reuse. Many deep learning share many common data pre-process steps. User needn't write them every time, but can reuse others work.

Transformer can be used with RDD(rdd.mapPartition), iterator and DataSet.

Linear Supertypes
Serializable, Serializable, AnyRef, Any
Known Subclasses
AspectScale, BGRImgCropper, BGRImgNormalizer, BGRImgPixelNormalizer, BGRImgRdmCropper, BGRImgToBatch, BGRImgToImageVector, BGRImgToLocalSeqFile, BGRImgToSample, Brightness, BytesToBGRImg, BytesToGreyImg, BytesToMat, CenterCrop, ChainedFeatureTransformer, ChainedTransformer, ChannelNormalize, ChannelOrder, ChannelScaledNormalizer, ClassificationMTImageFeatureToBatch, ColorJitter, ColorJitter, Contrast, Crop, DetectionCrop, Expand, FeatureTransformer, Filler, FixExpand, FixedCrop, GreyImgCropper, GreyImgNormalizer, GreyImgToBatch, GreyImgToSample, HFlip, HFlip, Hue, Identity, ImageFeatureToMiniBatch, ImageFrameToSample, LabeledSentenceToSample, Lighting, LocalResizeImgReader, LocalResizeImgWithName, LocalScaleImgReader, LocalScaleImgWithName, LocalSeqFileToBytes, MTImageFeatureToBatch, MTLabeledBGRImgToBatch, MatToFloats, MatToTensor, PixelBytesToMat, PixelNormalizer, RandomAlterAspect, RandomAspectScale, RandomCrop, RandomCropper, RandomResize, RandomSampler, RandomTransformer, Resize, RoiHFlip, RoiImageFeatureToBatch, RoiImageFeatureToBatchWithResize, RoiNormalize, RoiProject, RoiResize, RowToByteRecords, RowTransformer, SampleToBatch, SampleToMiniBatch, Saturation, ScaleResize, SentenceBiPadding, SentenceSplitter, SentenceTokenizer, TextToLabeledSentence, toAutoencoderBatch
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Abstract Value Members

  1. abstract def apply(prev: Iterator[A]): Iterator[B]

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Concrete Value Members

  1. final def !=(arg0: Any): Boolean

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    Definition Classes
    AnyRef → Any

  2. final def ##(): Int

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  3. def ->[C](other: Transformer[B, C]): Transformer[A, C]

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  4. final def ==(arg0: Any): Boolean

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  5. def apply(dataset: RDD[A])(implicit evidence: ClassTag[B]): RDD[B]

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    Apply this transformer to rdd

  6. final def asInstanceOf[T0]: T0

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  7. def clone(): AnyRef

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    protected[java.lang]
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    @throws( ... )

  8. def cloneTransformer(): Transformer[A, B]

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  9. final def eq(arg0: AnyRef): Boolean

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  10. def equals(arg0: Any): Boolean

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  11. def finalize(): Unit

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    @throws( classOf[java.lang.Throwable] )

  12. final def getClass(): Class[_]

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  13. def hashCode(): Int

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  14. final def isInstanceOf[T0]: Boolean

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  15. final def ne(arg0: AnyRef): Boolean

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  16. final def notify(): Unit

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  17. final def notifyAll(): Unit

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  18. final def synchronized[T0](arg0: ⇒ T0): T0

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  19. def toString(): String

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  20. final def wait(): Unit

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  21. final def wait(arg0: Long, arg1: Int): Unit

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  22. final def wait(arg0: Long): Unit

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Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

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